Mengyuan Tian1,2, Shujuan Ma3, Yiping You4, Sisi Long1,2, Jiayue Zhang1,2, Chuhao Guo1,2, Xiaolei Wang1,2, Hongzhuan Tan1,2. 1. Xiangya School of Public Health, Central South University, Changsha, China. 2. Hunan Key Laboratory of Clinical Epidemiology, Changsha, China. 3. Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha, China. 4. Department of Obstetrics, Hunan Provincial Maternal and Child Health Hospital, Changsha, China.
Abstract
OBJECTIVE: Gestational diabetes mellitus (GDM) is a common metabolic disorder with onset during pregnancy. However, the etiology and pathogenesis of GDM have not been fully elucidated. In this study, we used a metabolomics approach to investigate the relationship between maternal serum metabolites and GDM in early pregnancy. METHODS: A nested case-control study was performed. To establish an early pregnancy cohort, pregnant women in early pregnancy (10-13+6 weeks) were recruited. In total, 51 patients with GDM and 51 healthy controls were included. Serum samples were analyzed using an untargeted high-performance liquid chromatography mass spectrometry metabolomics approach. The relationships between metabolites and GDM were analyzed by an orthogonal partial least-squares discriminant analysis. Differential metabolites were evaluated using a KEGG pathway analysis. RESULTS: A total of 44 differential metabolites were identified between GDM cases and healthy controls during early pregnancy. Of these, 26 significant metabolites were obtained in early pregnancy after false discovery rate (FDR < 0.1) correction. In the GDM group, the levels of L-pyroglutamic acid, L-glutamic acid, phenylacetic acid, pantothenic acid, and xanthine were significantly higher and the levels of 1,5-anhydro-D-glucitol, calcitriol, and 4-oxoproline were significantly lower than those in the control group. These metabolites were involved in multiple metabolic pathways, including those for amino acid, carbohydrate, lipid, energy, nucleotide, cofactor, and vitamin metabolism. CONCLUSIONS: We identified significant differentially expressed metabolites associated with the risk of GDM, providing insight into the mechanisms underlying GDM in early pregnancy and candidate predictive markers.
OBJECTIVE: Gestational diabetes mellitus (GDM) is a common metabolic disorder with onset during pregnancy. However, the etiology and pathogenesis of GDM have not been fully elucidated. In this study, we used a metabolomics approach to investigate the relationship between maternal serum metabolites and GDM in early pregnancy. METHODS: A nested case-control study was performed. To establish an early pregnancy cohort, pregnant women in early pregnancy (10-13+6 weeks) were recruited. In total, 51 patients with GDM and 51 healthy controls were included. Serum samples were analyzed using an untargeted high-performance liquid chromatography mass spectrometry metabolomics approach. The relationships between metabolites and GDM were analyzed by an orthogonal partial least-squares discriminant analysis. Differential metabolites were evaluated using a KEGG pathway analysis. RESULTS: A total of 44 differential metabolites were identified between GDM cases and healthy controls during early pregnancy. Of these, 26 significant metabolites were obtained in early pregnancy after false discovery rate (FDR < 0.1) correction. In the GDM group, the levels of L-pyroglutamic acid, L-glutamic acid, phenylacetic acid, pantothenic acid, and xanthine were significantly higher and the levels of 1,5-anhydro-D-glucitol, calcitriol, and 4-oxoproline were significantly lower than those in the control group. These metabolites were involved in multiple metabolic pathways, including those for amino acid, carbohydrate, lipid, energy, nucleotide, cofactor, and vitamin metabolism. CONCLUSIONS: We identified significant differentially expressed metabolites associated with the risk of GDM, providing insight into the mechanisms underlying GDM in early pregnancy and candidate predictive markers.
Authors: F Zhang; L Dong; C P Zhang; B Li; J Wen; W Gao; S Sun; F Lv; H Tian; J Tuomilehto; L Qi; C L Zhang; Z Yu; X Yang; G Hu Journal: Diabet Med Date: 2011-06 Impact factor: 4.359
Authors: Daniel Sachse; Line Sletner; Kjersti Mørkrid; Anne Karen Jenum; Kåre I Birkeland; Frode Rise; Armin P Piehler; Jens Petter Berg Journal: PLoS One Date: 2012-12-21 Impact factor: 3.240
Authors: Abdalla Ahmed Elamin; Mohammed Noah Mohammed Ahmed; Abubaker El Elhaj; Tarig Mahmoud Ahmed Hussien; Abdelrahim Awadelkarim Abdelrahman Mohamed; Hamza Mohamed; Saadeldin Ahmed Idris Journal: Int J Appl Basic Med Res Date: 2022-01-31